An ACRL white paper from 2012 reported that, at that time, only a small number of academic libraries in the United States and Canada offered research data services (RDS), but many were planning to do so within the next two years (Tenopir, Birch, and Allard, 2012). By 2013, 74% of the Association of Research Libraries (ARL) survey respondents offered RDS and an additional 23% were planning to do so (Fearon, Gunia, Pralle, Lake, and Sallans, 2013). The academic libraries recognize that the landscape of services changes quickly and that they need to support the changing needs of research and instruction. In their efforts to implement RDS, libraries often respond to pressures originating outside the library, such as national or funder mandates for data management planning and data sharing. To provide effective support for researchers and instructors, though, libraries must be proactive and develop new services that look forward and yet accommodate the existing human, technological, and intellectual capital accumulated over the decades. Setting the stage for data curation in libraries means to create visionary approaches that supersede institutional differences while still accommodating diversity in implementation. How do academic libraries work towards that? This chapter will combine an historical overview of RDS thinking and implementations based on the existing literature with an empirical analysis of ARL libraries’ current RDS goals and activities. The latter is based on the study we conducted in 2015 that included a content analysis of North American research library web pages and interviews of library leaders and administrators of ARL libraries. Using historical and our own data, we will synthesize the current state of RDS implementation across ARL libraries. Further, we will examine the models of research data management maturity (see, for example, Qin, Crowston and Flynn, 2014) and discuss how such models compare to our own three-level classification of services and activities offered at libraries - basic, intermediate, and advanced. Our analysis will conclude with a set of recommendations for next steps, i.e., actions and resources that a library might consider to expand their RDS to the next maturity level. References Fearon, D. Jr., Gunia, B., Pralle, B.E., Lake, S., Sallans, A.L. (2013). Research data management services. (ARL Spec Kit 334). Washington, D.C.: ARL. Retrieved from: http://publications.arl.org/Research-Data-Management-Services-SPEC-Kit-334/ Tenopir, C., Birch, B., & Allard, S. (2012). Academic libraries and research data services: Current practices and plans for the future. ACRL. Retrieved from http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/whitepapers/Tenopir_Birch_Allard.pdf Qin, J., Crowston, K., & Flynn, C. (2014). 1.1 Commitment to Perform. A Capability Maturity Model for Research Data Management. wiki. Retrieved http://rdm.ischool.syr.edu/xwiki/bin/view/CMM+for+RDM/WebHome

The academic library profession has discussed the importance of diversity and inclusion, but has yet to explore their intersection with social media marketing. Given changing demographics and an increase in activism on college campuses, libraries must produce social media content that resonates with underrepresented groups. This chapter introduces strategies for effectively incorporating diversity and inclusion into social media and advice on avoiding mistakes. The author examines social media use at her library and lessons learned from a campaign that received criticism. The chapter concludes with challenges to practicing diversity in social media, including discrimination that emerges from new social media technologies and the recruitment of diverse staff.

In this chapter, we investigate the most recent automatic detecting algorithms on abnormal electrocardiogram (ECG) in a variety of cardiac arrhythmias. We present typical examples of a medical case study and technical applications related to diagnosing ECG, which include (i) a recently patented data classifier on the basis of deep learning model, (ii) a deep neural network scheme to diagnose variable types of arrhythmia through wearable ECG monitoring devices, and (iii) implementation of the health cloud platform, which consists of automatic detection, data mining, and classifying via the Android terminal module. Our work establishes a cross-area study, which relates artificial intelligence (AI), deep learning, cloud computing on huge amount of data to minishape ECG monitoring devices, and portable interaction platforms. Experimental results display the technical advantages such as saving cost, better reliability, and higher accuracy of deep learning-based models in contrast to conventional schemes on cardiac diagnosis.

Noninvasive detection of fetal electrocardiogram (FECG) from abdominal ECG recordings is highly dependent on typical statistical signal processing techniques such as independent component analysis (ICA), adaptive noise filtering, and multichannel blind deconvolution. In contrast to the previous multichannel FECG extraction methods, several recent schemes for single-channel FECG extraction such as the extended Kalman filter (EKF), extended Kalman smoother (EKS), template subtraction (TS), and support vector regression (SVR) for detecting R waves on ECG, are evaluated via the quantitative metrics such as sensitivity (SE), positive predictive value (PPV), F-score, detection error rate (DER), and range of accuracy. A correlation predictor that combines with multivariable gray model (GM) is also proposed for sequential ECG data compression, which displays better percent root mean-square difference (PRD) than those of Sabah’s scheme for fixed and predicted compression ratio (CR). Automatic calculation on fetal heart rate (FHR) on the reconstructed FECG from mixed signals of abdominal ECG recordings is also experimented with sample synthetic ECG data. Sample data on FHR and T/QRS for both physiological case and pathological case are simulated in a 10-min time sequence.

Universities are increasing efforts to assist faculty, students, and community partners to bring inventions to market. As state funding for higher education continues to shrink, universities are looking for new revenue streams to support teaching, research, and learning. Campus commercialization efforts are a strategy to promote economic development opportunities that benefit the community and society at large. Commercialization efforts on campus include technology transfer offices, incubator parks, and joint ventures between the university, investors, and corporate entities. Academic libraries have the opportunity to partner with campus technology transfer efforts and expand librarian roles and responsibilities to support commercialization, innovation,and entrepreneurship on campus

Expansion of agricultural practices from the Fertile Crescent to China during the mid and late Holocene are believed to have shaped the early network of Silk Road routes and possibly regulated the dynamics of trade and exchange in the urban oases along the Silk Road throughout its existence. While the impacts of climate change on the Silk Road are more or less documented for the medieval period, they remain poorly understood for early history of the Silk Road, especially in Central Asia. We analyze hydroclimatic proxies derived from fluvial stratigraphy, geochronology, and tree-ring records that acted on various time scales in the Lake Balkhash Basin to learn how changes in water supply could have influenced the early farmers in the Semirechye region of southern Kazakhstan. Our approach aims to identify short-term and long-term variability of regional runoff and to compare the hydrological data with cultural dynamics coupled with the archaeological settlement pattern and agricultural production. The reconstructed runoff variability underscore the contribution of winter precipitation driven by the interaction between the Arctic oscillation and the Siberian High-Pressure System, to Central Asian river discharge. We show that Saka people of the Iron Age employed extensive ravine agriculture on the alluvial fans of the Tian Shan piedmont, where floodwater farming peaked between 400 BC and 200 BC. The early Silk Road farmers on the alluvial fans favored periods of reduced flood flows, river stability and glacier retreat in the Tian Shan Mountains. Moreover, they were able to apply simple flow control structures to lead water across the fan surface. It is very unlikely that changes in water supply ever significantly constricted agricultural expansion in this region.